Lubricant, grease and industrial fluid formulation research has long been acknowledged as a combination of art and science. Formulation research presents a nearly overwhelming number of variables for each possible application. Even within a given application area, a wide variety of base fluids may be used. For instance, base fluids are produced to meet required specifications. These base oils are classified by the American Petroleum Institute (API) as Group I, Group II, Group III, Group IV and Group V, which designate parametric boundaries for viscosity index, sulfur content, amount of non-paraffins and the like. However, the actual chemical composition of a base oil that meets a specific API group criteria may vary significantly from base oil to base oil.
Compounding these issues are the vast variety of chemical additives which have become necessary components in today's modern lubricants. For example, lubricants commonly include additives for corrosion control, metal passivation, extreme pressure resistance, viscosity modification, detergency, acid control, etc. While one might correctly assume that the chemistries among these functional groups may vary widely, it is also recognized that the chemistries within each of these functional additive groups may vary significantly. While the properties of any one additive in any one base oil may be relatively well known, combining additives may have unexpected (beneficial or undesired) chemical interactions.
Lubricant research might be somewhat simplified if it was limited to solving this myriad of chemical interactions between additives and base oils. But in the real world, varying engine configurations presents unique flow and heat transfer properties that cause even a standardized lubricant to react differently. Currently, equipment manufacturers require that actual engine or machinery tests verify the applicability of a candidate lubricant formulation. Indeed, many Original Equipment Manufacturers (OEM) of engines or other equipment that employ lubricants, greases or functional fluids, have their own unique test to “qualify” the candidate product. Tests such as the European Union Association des Constructeurs Europeens d'Automobiles (ACEA) standards, or the United States American Petroleum Instititute (API) and International Lubricant Standards Approval Committee (ILSAC) standards require large quantities of the candidate fluids tested over weeks of time under actual full-scale engine conditions. These tests are time consuming and costly.
Lubricant researchers often employ a number of lowest-common-denominator bench tests to attempt to predict how a lubricant would fare in real-world conditions. Such bench tests are designed to provide in a laboratory environment a measure of a property or performance feature of a lubricant sample. The researcher attempts to use the bench test to make a laboratory model of the conditions of actual engines or equipment. Usually, the scope of the bench tests is limited to attempting to re-create one specific aspect of the equipment's operating environment. Not being able to exactly match the intense pressure, heat, friction, load and other conditions of operating equipment, researchers make assumptions to design bench tests to isolate the variable of interest. Unfortunately, it is generally acknowledged that bench tests are, at best, weakly predictive of the single dimension of equipment conditions they attempt to mimic.
Examples of these tests are as follows: ASTM D2266 (Four Ball method for wear preventive characteristics of lubricating grease), ASTM D2272 (Oxidation stability of by rotating bomb), ASTM D2596 (Four Ball method for measurement of load carrying capacity of lubricating grease), ASTM D2783 (Four Ball method for measurement of extreme-pressure properties of lubricating fluids), ASTM D4172 (Four Ball method for wear preventive characteristics of lubricating fluids), ASTM D4742 (Thin-film oxygen uptake test), ASTM D6138 (Emcor test for determination of corrosion preventive properties of lubricating grease under dynamic wet conditions), ASTM D6186 (Pressure differential scanning calorimetry method for oxidation induction time of lubricating oils) along with the numerous other tests specified in various lubricant oil or grease specifications.
These tests too often show poor correlation to real-world results. Since these tests tend to investigate along a single dimension, they limit opportunities to discover positive or negative chemical interactions. Moreover, it is difficult, if not impossible, to determine which combination of tests, if any, would predict a binary pass/fail result for any specific OEM's end-use test. These tests would most likely not give a graduated view of which base oils, additives or formulations would better pass a given OEM end-use test.
The present invention addresses these, and many other issues. Specifically, the present invention provides a method to determine which laboratory scale tests are predictive of real-world results or OEM end-use tests. While the inventors believe that best candidate laboratory-scale tests would be those that produce significant amounts of data, the present invention also details a method of using data in currently existing databases to predict which test, or functional combination of tests, would best predict OEM end-use tests or real world performance results.
One feature of the present invention is that it provides a means to determine which tests, useful in a laboratory setting, would be sufficient to predict the desired end-use qualifying test results. Another feature of the present invention is that it demonstrates a method and a device to predict and select voluminous data-producing tests that will mimic the lubricant bench test results or the end-use qualifying test results. As a corollary, the present invention provides a method to determine which tests, capable of being used in a high-throughput environment, are able to predict end-use qualification test results for lubricants oils and greases. Still yet another feature of the present invention is its ability to employ historical databases of bench test results to select combinations of those bench tests, with and without high-throughput tests, that more accurately predict the end-use qualifying test results.
One embodiment of the present invention employs pattern recognition-based modeling to guide adaptive learning systems to derive correlative models by learning from data. The present invention's use of iterative learning leads to converged functional classifications and/or correlations between independent and dependent variables.
Throughout this application, the inventors use of the word lubricant (or its derivatives) also refers to lubricants, greases, and various types of functional fluids (and their respective derivatives).
The current state of the art for the formulation of lubricants requires extensive formulator experience to select the optimum combination of additives and base stocks. The possible combinatorial space is quite large consisting of many different base oils and “functional families” of additives (e.g., antiwear, antioxidants, antifoaming, viscosity modifiers, dispersants, thickeners, detergents, etc.). Each functional family contains numerous different chemistries to achieve the desired function. Further complicating the formulation discovery process is that the base oils for the lubricant vary widely from highly naphthenic API Group I base oils to high purity PAO to even non-hydrocarbon based fluids such as silicones. Another complication is that the additive functional families may react differently to different base oil combinations. Indeed, one other well-known problem is that lubricant formulation chemistries are not always linear—that is, an interpolated blend of two successful lubricant chemistries does not always produce a product able to pass the same tests.
Creating a new or “step-out” lubricant formulation is severely limited by the extensive in-place engine or machinery testing that each successful candidate lubricant must pass. On average, each individual test costs between $10,000-$150,000. Sole reliance on expensive, large-scale testing to develop new lubricants (and greases), results more often in incremental formulation improvements and limits the inclusion of new, experimental components in new formulations since they require more extensive testing. Overall, sole reliance on expensive large-scale testing confines experimentation more often to the limited known-formulation performance, and it is likely that opportunities for step-out improvements in formulation technology for lubricants, functional fluids or greases are not captured.
The introduction of intermediate bench tests to lubricant formulation research can further complicate the process. Lubricant bench-tests attempt to mimic essential portions of the engine's or industrial equipment's operation, usually limiting themselves to a single dimension (e.g. acid value increase in a stability test at certain temperature. For example, engines may vary significantly within a product category (commercial, personal vehicle, aviation, marine or stationary industrial engines), let alone compared to other types of equipment such as, gearboxes, pumps, compressors, circulating systems and others. Any individual bench test predictive for any one engine is almost certainly not predictive of other engines or machinery.
A further complication is that equipment manufacturer's lubricant qualification tests differ even for similar equipment and are often changed on a frequent basis to reflect updated equipment technology. Typically, lubricant bench-testing is called upon to predict a large range of possible outcomes. While lubricant bench-tests are intended to allow an inexpensive measure of predictability for the more expensive large-scale tests, understanding and interpreting the correlations between bench tests and the final engine or machinery tests has often proven to be difficult. Years of experience, combined with a formulator's intuition, can help to link a successful set of bench tests to a successful large-scale, end-use test or tests. However, even upon entering the 21st century, the formulation of lubricants, functional fluids or greases remains both an art and a science.
Among other features, the present invention provides a unique opportunity to apply high throughput techniques to lubricant research. Researchers have traditionally attempted to use a series of bench tests to determine the potential performance of a formulated lubricant, functional fluid or grease candidate in end-use tests. Commonly used bench tests include wear, viscosity, thermal and oxidative stability, deposit control, elastomer compatibility, filterability, friction, volatility, foam and air release, corrosion and rust, miscibility, solubility and homogeneity and visual appearance. However, these bench tests were seldom adaptable to producing large amounts of data in short periods of time as they often required large lubricant sample volumes, long test times, severe test conditions or combinations of all three. Even if one could easily adapt these bench tests to produce large volumes of data in a short period of time, there is no reason to believe that they would correctly predict actual success on end-use qualifying tests.